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Stepwise approach to SNP-set analysis illustrated with the Metabochip and colorectal cancer in Japanese Americans of the Multiethnic Cohort

Overview of attention for article published in BMC Genomics, July 2018
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Title
Stepwise approach to SNP-set analysis illustrated with the Metabochip and colorectal cancer in Japanese Americans of the Multiethnic Cohort
Published in
BMC Genomics, July 2018
DOI 10.1186/s12864-018-4910-8
Pubmed ID
Authors

John Cologne, Lenora Loo, Yurii B. Shvetsov, Munechika Misumi, Philip Lin, Christopher A. Haiman, Lynne R. Wilkens, Loïc Le Marchand

Abstract

Common variants have explained less than the amount of heritability expected for complex diseases, which has led to interest in less-common variants and more powerful approaches to the analysis of whole-genome scans. Because of low frequency (low statistical power), less-common variants are best analyzed using SNP-set methods such as gene-set or pathway-based analyses. However, there is as yet no clear consensus regarding how to focus in on potential risk variants following set-based analyses. We used a stepwise, telescoping approach to analyze common- and rare-variant data from the Illumina Metabochip array to assess genomic association with colorectal cancer (CRC) in the Japanese sub-population of the Multiethnic Cohort (676 cases, 7180 controls). We started with pathway analysis of SNPs that are in genes and pathways having known mechanistic roles in colorectal cancer, then focused on genes within the pathways that evidenced association with CRC, and finally assessed individual SNPs within the genes that evidenced association. Pathway SNPs downloaded from the dbSNP database were cross-matched with Metabochip SNPs and analyzed using the logistic kernel machine regression approach (logistic SNP-set kernel-machine association test, or sequence kernel association test; SKAT) and related methods. The TGF-β and WNT pathways were associated with all CRC, and the WNT pathway was associated with colon cancer. Individual genes demonstrating the strongest associations were TGFBR2 in the TGF-β pathway and SMAD7 (which is involved in both the TGF-β and WNT pathways). As partial validation of our approach, a known CRC risk variant in SMAD7 (in both the TGF-β and WNT pathways: rs11874392) was associated with CRC risk in our data. We also detected two novel candidate CRC risk variants (rs13075948 and rs17025857) in TGFBR2, a gene known to be associated with CRC risk. A stepwise, telescoping approach identified some potentially novel risk variants associated with colorectal cancer, so it may be a useful method for following up on results of set-based SNP analyses. Further work is required to assess the statistical characteristics of the approach, and additional applications should aid in better clarifying its utility.

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Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 40%
Student > Master 2 10%
Researcher 2 10%
Student > Bachelor 1 5%
Other 1 5%
Other 1 5%
Unknown 5 25%
Readers by discipline Count As %
Medicine and Dentistry 5 25%
Biochemistry, Genetics and Molecular Biology 4 20%
Mathematics 3 15%
Agricultural and Biological Sciences 1 5%
Neuroscience 1 5%
Other 0 0%
Unknown 6 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 July 2018.
All research outputs
#14,419,368
of 23,094,276 outputs
Outputs from BMC Genomics
#5,731
of 10,705 outputs
Outputs of similar age
#185,421
of 326,642 outputs
Outputs of similar age from BMC Genomics
#98
of 208 outputs
Altmetric has tracked 23,094,276 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,705 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 326,642 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 208 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.